Multi-Scale Machine Learning for the Classification of Building Property Values

Autor:	Benjamin Bischke, Patrick Helber, Qiushi Guo, Andreas Dengel, Jörn Hees
Rok vydání:	2019
Předmět:	Scale (ratio) business.industry Property value Computer science Deep learning Artificial intelligence business Machine learning computer.software_genre Baseline (configuration management) Proxy (statistics) Convolutional neural network computer
Zdroj:	IGARSS
DOI:	10.1109/igarss.2019.8900257
Popis:	In this paper, we describe a multi-scale machine learning approach to estimate socio-economic attributes of citizens based on the analysis of aerial images. To analyse the effectiveness of the proposed approach we predict building property value classes. The classification of these building property values is a proxy for the socio-economic status of the residents. The approach is based on the fusion of deep Convolutional Neural Networks (CNNs). We compare the proposed approach with non-image and single-scale CNN approaches and demonstrate the effectiveness in a case study using statistical data collected in the city of Amsterdam, Netherlands. We show that the proposed multi-scale approach outperforms the baseline methods.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::356becab59037baa2b099452104276b2 https://doi.org/10.1109/igarss.2019.8900257 Zobrazit plný text záznamu